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Due to amount of today's electricity consumption, one of the most important tasks of the energy operators is to be able to predict the consumption and be ready to control the energy generation based on the estimated consumption for the future. In this way, having a trustable forecast of the electricity consumption is essential to control the consumption and maintain the balance in energy distribution networks. This study presents a day ahead forecasting approach based on a genetic fuzzy system for fuzzy rule learning based on the MOGUL methodology (GFS.FR.MOGUL). The proposed approach is used to forecast the electricity consumption of an office building in the following 24 hours. The goal of this work is to present a more reliable profile of the electricity consumption comparing to previous works. Therefore, this paper also includes the comparison of the results of day ahead forecasting using GFS.FR.MOGUL method against other fuzzy rule based methods, as well as a set of Artificial Neural Network(ANN) approaches. This comparison shows that using the GFS.FR.MOGUL forecasting method for day-ahead electricity consumption forecasting is able to estimate a more trustable value than the other approaches. ; This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion

The increasing penetration of distributed energy sources, mainly based on renewable generation, calls for an urgent emergence of novel advanced methods to deal with the associated problems. The consensus behind smart grids (SGs) as one of the most promising solutions for the massive integration of renewable energy sources in power systems has led to the development of several prototypes that aim at testing and validating SG methodologies. The urgent need to accommodate such resources require alternative solutions. This chapter presents a multi-agent based SG simulation platform connected to physical resources, so that realistic scenarios can be simulated. The SG simulator is also connected to the Multi-Agent Simulator of Competitive Electricity Markets, which provides a solid framework for the simulation of electricity markets. The cooperation between the two simulation platforms provides huge studying opportunities under different perspectives, resulting in an important contribution to the fields of transactive energy, electricity markets, and SGs. A case study is presented, showing the potentialities for interaction between players of the two ecosystems: a SG operator, which manages the internal resources of a SG, is able to participate in electricity market negotiations to trade the necessary amounts of power to fulfill the needs of SG consumers. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement N. 641794 (project DREAM-GO). It has also received FEDER Funds through the COMPETE program and National Funds through FCT under the project UID/EEA/00760/2013. The authors gratefully acknowledge the valuable contribution of Bruno Canizes, Daniel Paiva, Gabriel Santos and Marco Silva to the work presented in the chapter. ; info:eu-repo/semantics/publishedVersion

This paper proposes a model based on particle swarm optimization to aid electricity markets players in the selection of the best player(s) to trade with, to maximize their bilateral contracts outcome. This approach is integrated in a Decision Support System (DSS) for the pre-negotiation of bilateral contracts, which provides a missing feature in the state-of-art, the possible opponents analysis. The DSS determines the best action of all the actions that the supported player can take, by applying a game theory approach. However, the analysis of all actions can easily become very time-consuming in large negotiation scenarios. The proposed approach aims to provide the DSS with an alternative method with the capability of reducing the execution time while keeping the results quality as much as possible. Both approaches are tested in a realistic case study where the supported player could take almost half a million different actions. The results show that the proposed methodology is able to provide optimal and near-optimal solutions with an huge execution time reduction. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); from the CONTEST project - SAICT-POL/23575/2016; and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

A key challenge in the power and energy field is the development of decision-support systems that enable studying big problems as a whole. The interoperability between systems that address specific parts of the global problem is essential. Ontologies ease the interoperability between heterogeneous systems providing semantic meaning to the information exchanged between the various parties. The use of ontologies within Smart Grids has been proposed based on the Common Information Model, which defines a common vocabulary describing the basic components used in electricity transportation and distribution. However, these ontologies are focused on utilities needs. The development of ontologies that allow the representation of diverse knowledge sources is essential, aiming at supporting the interaction between entities of different natures, facilitating the interoperability between these systems. This paper proposes a set of ontologies to enable the interoperability between different types of simulators, namely regarding electricity markets, the smart grid, and residential energy management. A case study based on real data shows the advantages of the proposed approach in enabling comprehensive power system simulation studies. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT), from the EUREKA - ITEA2 Project M2MGrids (ITEA-13011), Project SIMOCE (ANI—P2020 17690), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

The electricity markets have been suffering profound changes over the years. Nowadays, the European Union comes to reformulate its politics related with renewable energy sources, in order to encourage microgeneration, having demand response as one of the biggest challenges. Various simulators have been developed that intend to give decision support to the various entities. However, they present the limitation of being designed to answer specific problems. This paper proposes the framework Tools Control Center (TOOCC) as the mechanism to integrate various independent and heterogeneous simulators, so they operate as a unique simulation tool and become capable of answering to more complex problems. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

This paper proposes a coalitional game-theoretical model for consumers' flexibility coalition formation, supported by an optimization model based on differential evolution. Traditionally, the participation in conventional electricity markets used to be limited to large producers and consumers. The final end-users contract their energy supply with retailers, since due to the smaller quantity available for trading, they cannot participate in electricity market transactions. Nowadays, the growing concept of local electricity market brings many advantages to the end-users. The flexibility negotiation considering local areas is an important procedure for network operators and it is incorporating a local electricity market opportunity. A coalition formation model to facilitate small players participation in the flexibility market proposed by the network operator is addressed in this work. The inclusion of Shapley value in the proposed model enables finding the best coalition structures considering the fairness of the coalitions in addition to the potential income achieved by the consumers when selling their flexibility. An optimization model based on differential evolution is also proposed as the way to find the optimal coalition structures based on the multi-criteria specifications. ; This work has received funding from FEDER Funds through COMPETE program and from National Funds through (FCT) under the projects CEECIND/01811/2017 and UIDB/00760/2020, from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066), and Ricardo Faia is supported by national funds through Fundacão para a Ciência e a Tecnologia (FCT) with PhD grant reference SFRH/BD/133086/2017. ; info:eu-repo/semantics/publishedVersion

This paper proposes a case-based reasoning methodology to automatically choose the most appropriate optimization algorithms and respective parameterizations to solve the problem of optimal resource scheduling in smart energy grids. The optimal resource scheduling is, however, a heavy computation problem, which deals with a large number of variables. Moreover, depending on the time horizon of this optimization, fast response times are usually required, which makes it impossible to apply traditional exact optimization methods. For this reason, the application of metaheuristic methods is the natural solution, providing near-optimal solutions in a much faster execution time. Choosing which optimization approaches to apply in each time is the focus of this work, considering the requirements for each problem and the information of previous executions. A case-based reasoning methodology is proposed, considering previous cases of execution of different optimization approaches for different problems. A fuzzy logic approach is used to adapt the solutions considering the balance between execution time and quality of results ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 (project DREAM-GO) and a grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion

This paper presents the application of a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) to forecast energy consumption. Historical data referring to the energy consumption gathered from three groups, namely lights, HVAC and electrical socket, are used to train the proposed approach and achieve forecasting results for the future. The performance of the proposed method is compared to that of previous approaches, namely Hybrid Neural Fuzzy Interface System (HyFIS) and Wang and Mendel's Fuzzy Rule Learning Method (WM). Results show that the proposed methodology achieved smaller forecasting errors for the following hours, with a smaller standard deviation. Thus, the proposed approach is able to achieve more reliable results than the other state of the art methodologies ; This work has been developed under the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO). ; info:eu-repo/semantics/publishedVersion

Due to the increment of the energy consumption and dependency of the nowadays lifestyle to the electrical appliances, the essential role of an energy management system in the buildings is realized more than ever. With this motivation, predicting energy consumption is very relevant to support the energy management in buildings. In this paper, the use of an energy management system supported by forecasting models applied to energy consumption prediction is demonstrated. The real-time automatic forecasting system is running separately but integrated with the existing SCADA system. Nine different forecasting approaches to obtain the most reliable estimated energy consumption of the building during the following hours are implemented. ; The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 project DOMINOES (grant agreement No 771066), COLORS Project PTDC/EEI-EEE/28967/2017 and UID/EEA/00760/2019 funded by FEDER Funds through COMPETE program and by National Funds through FCT. ; info:eu-repo/semantics/publishedVersion

The use of energy from renewable sources is one of the major concerns of today's society. In recent years, the European Union has been changing legislation and implementing policies aimed at promoting its investment and encouraging its use in order to reduce the emission of greenhouse gases [1]. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO), from the CONTEST project - SAICT-POL/23575/2016; and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

Worldwide electricity markets are undergoing a major restructuring process. One of the main reasons for the ongoing changes is to enable the adaptation of current market models to the new paradigm that arises from the large-scale integration of distributed generation sources. In order to deal with the unpredictability caused by the intermittent nature of the distributed generation and the large number of variables that contribute to the energy sector balance, it is extremely important to use simulation systems that are capable of dealing with the required complexity. This paper presents the Tools Control Center (TOOCC), a framework that allows the interoperability between heterogeneous energy and power simulation systems through the use of ontologies, allowing the simulation of scenarios with a high degree of complexity, through the cooperation of the individual capacities of each system. A case study based on real data is presented in order to demonstrate the interoperability capabilities of TOOCC. The simulation considers the energy management of a microgrid of a real university campus, from the perspective of the network manager and also of its consumers/producers, in a projection for a typical day of the winter of 2050. ; This work has been developed in the scope of the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 641794 (project DREAM-GO); CONTEST project - SAICT-POL/23575/2016; and has also been supported by FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

A physical smart city model environment is used to presents the demonstration of an energy resources management approach. The demand for smart cities has been created by several factors from the governments, society and industry. Thus, smart grids focus on the intelligent management of energy resources in order to maximize the usage of the energy from renewable sources in order to the final consumers feel the positive effects of less expensive (and pollutant) energy sources, namely in their energy bills. A large amount of work is being developed in the energy resources management domain, but an effective and realistic experimentation are still missing. This paper presents a realistic and physical experimentation of the energy resource management. This is done by using a physical smart city model, which includes several consumers, generation units, and electric vehicles. ; This work has received funding from the European Union's Horizon 2020 research and innovation program under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. Bruno Canizes is supported by FCT Funds through the SFRH/BD/110678/2015 PhD scholarship. ; info:eu-repo/semantics/publishedVersion

The environmental impact and the scarcity of limited fossil fuels led to the need of investment in energy based on renewable sources. This has driven Europe to implement several policies that changed the energy market's paradigm, namely the incentive to microgeneration. The penetration of energy sources from intermittent nature has increased the unpredictability of the system, which makes simulation and analysis tools essential in order to provide decision support to entities in this sector. This paper presents the Tools Control Center (TOOCC) as a solution to increase the interoperability between heterogeneous agent-based systems, in the energy field. The proposed approach acts as a facilitator in the interaction between different systems through the usage of ontologies, allowing them to communicate in the same language. To understand the real applicability of this tool, a case study is presented concerning the interaction between several systems, with the purpose of enabling the energy resource scheduling of a microgrid, and the reaction of a house managed by a house management system. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013. ; info:eu-repo/semantics/publishedVersion

One of the most challenging tasks for energy domain stakeholders is to have a better preview of the electricity consumption. Having a more trustable expectation of electricity consumption can help minimizing the cost of electricity and also enable a better control on the electricity tariff. This paper presents a study using a Methodology to Obtain Genetic fuzzy rule-based systems Under the iterative rule Learning approach (MOGUL) methodology in order to have a better profile of the electricity consumption of the following hours. The proposed approach uses the electricity consumption of the past hours to forecast the consumption value for the following hours. Results from this study are compared to those of previous approaches, namely two fuzzy based systems: and several different approaches based on artificial neural networks. The comparison of the achieved results with those achieved by the previous approaches shows that this approach can calculate a more reliable value for the electricity consumption in the following hours, as it is able to achieve lower forecasting errors, and a less standard deviation of the forecasting error results ; The present work was done and funded in the scope of the following projects: European Union's Horizon 2020 research and innovation programme, under the Marie Sklodowska-Curie grant agreement No 703689 (project ADAPT); EUREKA - ITEA2 Project FUSE-IT (ITEA-13023), Project GREEDI (ANI|P2020 17822), and has received funding from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion

The use of Decision Support Systems (DSS) in the eld of Electricity Markets (EM) is essential to provide strategic support to its players. EM are constantly changing, dynamic environments, with many entities which give them a particularly complex nature. There are several simulators for this purpose, including Bilateral Contracting. However, a gap is noticeable in the pre-negotiation phase of energy transactions, particularly in gathering information on opposing negotiators. This paper presents an overview of existing tools for decision support to the Bilateral Contracting in EM, and proposes a new tool that addresses the identied gap, using concepts related to automated negotiation, game theory and data mining. ; This work has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 641794 (project DREAM-GO) and grant agreement No 703689 (project ADAPT); and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2013 ; info:eu-repo/semantics/publishedVersion